---
title: "COVID-19 Ontario Cases"
author: "Francis McKenna"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
    self_contained: false
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
options(stringsAsFactors = FALSE)

library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)

# correct Ontario data
# confirmed cases

url <- "https://data.ontario.ca/dataset/f4f86e54-872d-43f8-8a86-3892fd3cb5e6/resource/ed270bb8-340b-41f9-a7c6-e8ef587e6d11/download/covidtesting.csv"
ont_data <- read.csv(url)

ont_data <- ont_data[,c("Reported.Date", "Total.Cases", "Deaths", "Resolved")]
names(ont_data) <- c("Date", "confirmed_cum", "death_cum", "resolved_cum")
ont_data[is.na(ont_data)] <- 0
ont_data$Date <- as.Date(ont_data$Date)

ont_data$active_cum <- ont_data$confirmed_cum - ont_data$death_cum

ont_data$confirmed <- ont_data$confirmed_cum - c(0, ont_data$confirmed_cum[-nrow(ont_data)])
ont_data$death <- ont_data$death_cum - c(0, ont_data$death_cum[-nrow(ont_data)])
ont_data$active <- ont_data$active_cum - c(0, ont_data$active_cum[-nrow(ont_data)])
ont_data$recovered <- ont_data$resolved_cum - c(0, ont_data$resolved_cum[-nrow(ont_data)])

fst_case <- ont_data$Date[min(which(ont_data$confirmed_cum > 0L))]
fst_death <- ont_data$Date[min(which(ont_data$death_cum > 0L))]

# load JHU Coronavirus data

url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
global_cases <- read.csv(url)

url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
global_deaths <- read.csv(url)

url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
global_recov <- read.csv(url)

library(reshape2)
coronavirus <- melt(global_cases, id.vars = c("Province.State", "Country.Region", "Lat", "Long"))
coronavirus$Date <- as.Date(coronavirus$variable, "X%m.%d.%y")
coronavirus$variable <- "confirmed"

tmp <- melt(global_deaths, id.vars = c("Province.State", "Country.Region", "Lat", "Long"))
tmp$Date <- as.Date(tmp$variable, "X%m.%d.%y")
tmp$variable <- "death"

coronavirus <- rbind(coronavirus, tmp)

tmp <- melt(global_recov, id.vars = c("Province.State", "Country.Region", "Lat", "Long"))
tmp$Date <- as.Date(tmp$variable, "X%m.%d.%y")
tmp$variable <- "recovered"

coronavirus <- rbind(coronavirus, tmp)

#library(coronavirus)
#data(coronavirus)
#update_datasets(silence = FALSE)

cond <- coronavirus$Province.State == "Ontario"
corona2 <- coronavirus[!cond,]

mn_dt <- min(c(corona2$Date, ont_data$Date))
mx_dt <- max(c(corona2$Date, ont_data$Date))

# confirmed first
tmp <- data.frame(Province.State = "Ontario",
            Country.Region = "Canada",
            Lat = 51.2538,
            Long = -85.3232,
            variable = "confirmed",
            value = 0L,
            Date = seq(mn_dt, mx_dt, 1L))

tmp$value <- ifelse(tmp$Date %in% ont_data$Date, 
                    ont_data$confirmed[match(tmp$Date, ont_data$Date)],
                    0L)

corona2 <- rbind(corona2, tmp)

# death next
tmp <- data.frame(Province.State = "Ontario",
                  Country.Region = "Canada",
                  Lat = 51.2538,
                  Long = -85.3232,
                  variable = "death",
                  value = 0L,
                  Date = seq(mn_dt, mx_dt, 1L))

tmp$value <- ifelse(tmp$Date %in% ont_data$Date, 
                    ont_data$death[match(tmp$Date, ont_data$Date)],
                    0L)

corona2 <- rbind(corona2, tmp)

# recovered next
tmp <- data.frame(Province.State = "Ontario",
                  Country.Region = "Canada",
                  Lat = 51.2538,
                  Long = -85.3232,
                  variable = "recovered",
                  value = 0L,
                  Date = seq(mn_dt, mx_dt, 1L))

tmp$value <- ifelse(tmp$Date %in% ont_data$Date, 
                    ont_data$recovered[match(tmp$Date, ont_data$Date)],
                    0L)

corona2 <- rbind(corona2, tmp)

names(corona2) <- c("Province.State", "Country.Region", "Lat", "Long", "type", "cases", "Date")


`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- corona2 %>%
  dplyr::filter(Country.Region == "Canada" & Province.State == "Ontario") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))


df_daily <- corona2 %>%
  dplyr::filter(Country.Region == "Canada" & Province.State == "Ontario") %>%
  dplyr::group_by(Date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(Date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active = confirmed - death) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
  )


df1 <- corona2 %>% dplyr::filter(Date == max(Date))

```

Summary
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```
















### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Death cases (death rate)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


Row
-----------------------------------------------------------------------

### **Daily cumulative cases by type** (Ontario only)
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~Date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmed",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~Date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Death",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_annotations(
    x = fst_case,
    y = 1,
    text = paste("First case"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = fst_death,
    y = 3,
    text = paste("First death"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Cumulative number of cases"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Comparison
=======================================================================


Column {data-width=400}
-------------------------------------


### **Comparison daily cumulative confirmed cases**
    
```{r}
daily_confirmed <- corona2 %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(Country.Region != "Canada" | Country.Region == "Canada" & Province.State == "Ontario") %>%
  dplyr::mutate(country = Country.Region) %>%
  dplyr::group_by(Date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)

daily_confirmed$Canada <- cumsum(daily_confirmed$Canada)

for(i in 2:length(daily_confirmed)) {
  tmp <- daily_confirmed[,i]
  tmp <- tmp[tmp >= 100]
  if (length(tmp) < nrow(daily_confirmed)) {
    daily_confirmed[,i] <- c(tmp, rep(NA, nrow(daily_confirmed) - length(tmp)))
  } else {
    daily_confirmed[,i] <- tmp[1:nrow(daily_confirmed)]
  }
}
daily_confirmed$Date <- 1:nrow(daily_confirmed)
cond <- apply(daily_confirmed[,names(daily_confirmed) %in% c("Canada", "US","Spain","Italy")], 1, function(x) any(!is.na(x)))
daily_confirmed <- daily_confirmed[cond,]

mx_x <- 1.2 * nrow(daily_confirmed)
mx_y <- 1000000

shapes <- list()
annot <- list()
for(i in 1:10) {
  #lbl <- paste0("p",r)
  r <- 0.05 * i
  x0 <- 0
  y0 <- 100
  x1 <- mx_x
  y1 <- 100 * exp(r * mx_x)
  if (y1 > mx_y) {
    x1 <- log(mx_y / 100) / r
    y1 <- mx_y
  }
  shapes[[i]] <- list(
    type = "line", layer = "below",
    xref = "x", yref = "y",
    #xsizemode = "scaled", ysizemode = "scaled",
    x0 = x0, x1 = x1, y0 = y0, y1 = y1,
    line = list(color = "grey", width = 2, dash = "dot")
  )
  annot[[i]] <- list(
    text = paste0("",round(100 * r, 1),"%"),
    xref = "x", yref= "y",
    x = x1, 
    y = log(y1, 10), 
    showarrow = FALSE,
    font = list(family = "Arial", size = 14, color = "grey")
  )
}


#----------------------------------------
# Plotting the data

daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~Date,
    y = ~Canada,
    type = "scatter",
    mode = "lines+markers",
    name = "Ontario"
  ) %>%
  plotly::add_trace(
    x = ~Date,
    y = ~US,
    type = "scatter",
    mode = "lines+markers",
    name = "United States"
  ) %>%
  plotly::add_trace(
    x = ~Date,
    y = ~Spain,
    type = "scatter",
    mode = "lines+markers",
    name = "Spain"
  ) %>%
  plotly::add_trace(
    x = ~Date,
    y = ~Italy,
    type = "scatter",
    mode = "lines+markers",
    name = "Italy"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of confirmed cases", 
                 type = "log", c(0, mx_y)),
    xaxis = list(title = "Days since 100 Cases or more", 
                 range = c(0, mx_x)),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    ),
    shapes = shapes,
    annotations = annot
  )
```


Column {data-width=400}
-------------------------------------

 
### **Cases distribution by type**

```{r daily_summary}
df_EU <- corona2 %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Canada" & Province.State == "Ontario" |
    Country.Region == "US" |
    Country.Region == "Italy" |
    Country.Region == "Spain") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Canada", "Ontario", country)) %>%  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))


plotly::plot_ly(
  data = df_EU,
  x = ~country,
  # y = ~unrecovered,
  y = ~ confirmed,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Confirmed",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases", type="log"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```

### **Logistic curve**


```{r logistic}
cond <- corona2$Province.State == "Ontario" & corona2$type == "confirmed"
df3 <- corona2[cond, c("Date", "cases")]
df3$cuml <- cumsum(df3$cases)
df3 <- df3[df3$cuml > 0,c("Date","cuml")]
df3 <- df3[!duplicated(df3$cuml),]
df3$X <- as.numeric(df3$Date - min(df3$Date))
df3$Y <- as.numeric(df3$cuml)
df3$X1 <- df3$X - 46

mod <- nls(Y ~ K * Y0 / (Y0 + (K - Y0)*exp(-r * X1)), data = df3[-(1:8),],
           start = c(K = 20000, r = 0.2, Y0 = 46))

pars <- mod$m$getAllPars()
K <- pars['K']
r <- pars['r']
Y0 <- pars['Y0']

df3$Ye <- K * Y0 / (Y0 + (K - Y0) * exp(-r * df3$X1))


# 
# 
# mod1 <- nls(I(log(Y)) ~ Const + r * X + I(log(K - Y + 0.1)),
#             data = df3[-(1:8),],
#             start = c(Const = 1, r = 0.18, K = 50000))
# 
# pars <- mod1$m$getAllPars()
# 
# a <- pars['K'] * exp(pars['Const'])
# r <- pars['r']
# b <- exp(pars['Const'])
# 
# df3$Ye <- a * exp(r * df3$X) / (1 + b * exp(r * df3$X))

# df3$Yp1 <- c(df3$Y[-1], NA)
# df3$Y2  <- df3$Y * df3$Y
# 
# mod2 <- lm(Yp1 ~ Y + Y2 + 0, data = df3)
# 
# r <- mod2$coefficients[1] - 1
# K <- -r / mod2$coefficients[2]
# 
# indx <- nrow(df3) %/% 2L
# X0 <- df3$X[indx]
# Y0 <- df3$Y[indx] #df3$Y[indx]
# 
# df3$X1 <- df3$X - X0 #- df3$X[indx]
# 
# df3$Ye <- K * Y0 / (Y0 + (K - Y0)*exp(-r*df3$X1))


addnl <- 1:30
tmp <- data.frame(Date = max(df3$Date) + addnl,
                  cuml = NA_integer_,
                  X = max(df3$X) + addnl,
                  Y = NA_real_,
                  X1 = max(df3$X1) + addnl)
# tmp$Ye <- a * exp(r * tmp$X) / (1 + b * exp(r * tmp$X))
tmp$Ye <-  K * Y0 / (Y0 + (K - Y0) * exp(-r * tmp$X1))

df3 <- rbind(df3, tmp)
names(df3) <- c("Date", "cuml", "X", "actual", "X1", "predicted")

df3 %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~Date,
    y = ~actual,
    type = "scatter",
    mode = "lines+markers",
    name = "Ontario actual"
  ) %>%
  plotly::add_trace(
    x = ~Date,
    y = ~predicted,
    type = "scatter",
    mode = "lines",
    name = "Ontario predicted"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Cumulative number of confirmed cases"),
    xaxis = list(title = "Days since first case"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )

```



Map
=======================================================================

### **World map of cases** (*use + and - icons to zoom in/out*)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- corona2 %>%
  # dplyr::filter(Country.Region == "Belgium") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>%
  #dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()

# need cumulative data for ontario

cond <- cv_data_for_plot$Province.State == "Ontario"
tmp <- cv_data_for_plot[cond,]
cv_data_for_plot <- cv_data_for_plot[!cond,]

tmp2 <- split(tmp, tmp$type)
tmp3 <- lapply(tmp2, 
               function(x) {
                 y <- x
                 y$cases <- cumsum(x$cases)
                 y
               })
tmp4 <- do.call("rbind", tmp3)

cv_data_for_plot <- rbind(cv_data_for_plot, tmp4)

cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        lng = ~Long, lat = ~Lat,
        #                 label=~as.character(cases),
        color = ~ pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "Country.Region", "Province.State")
        ),
        group = df,
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })

map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```





About
=======================================================================

**Modified for Ontario**

Added official Ontario results from the latest [public data on COVID-19 cases in Ontario](https://www.ontario.ca/page/2019-novel-coronavirus#section-0) and the [status dataset](https://data.ontario.ca/dataset/status-of-covid-19-cases-in-ontario).

Added logistic curve for Ontario.

```{r regression}
print(summary(mod))

```


**The Coronavirus Dashboard: the case of Belgium**

This Coronavirus dashboard: the case of Belgium provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Belgium. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.

**Code**

The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.

**Data**

The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data:

```
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
```

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.

**Contact**

For any question or feedback, you can [contact me](https://www.statsandr.com/contact/). More information about this dashboard can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).

**Update**

The data is as of `r format(max(coronavirus$Date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.